def process_tile(ii, reports=None):
global _SUCCS, _SHIFTS, _ANGLES, _SCALES, _DIFFS
tile = _TILES[ii]
image = _IMAGE
opts = _OPTS
pos = _POSS[ii]
try:
# TODO: Add unittests that zero success result
# doesn't influence anything
with reporting.report_wrapper(reports, ii) as wrapped:
resdict = process_images((tile, image), opts,
reports=wrapped)
resdict['tvec'] += pos
if np.isnan(_DIFFS[0]):
_DIFFS[0] = resdict["Dangle"]
_DIFFS[1] = resdict["Dscale"]
_DIFFS[2] = resdict["Dt"]
except ValueError:
# probably incompatible images due to high scale change, so we
# just add some harmless stuff here and proceed.
resdict = dict(success=0)
_distribute_resdict(resdict, ii)
_SUCCS[ii] = resdict["success"]
if _SUCCS[ii] > 0:
# print("%d: succ: %g" % (ii, resdict["success"]))
# import pylab as pyl
resdict["tvec"] -= pos
tosa = ird.transform_img_dict(image, resdict, 0, opts["order"])
tosa = utils.unextend_by(tosa, opts["extend"])
def process_tile(ii, reports=None):
global _SUCCS, _SHIFTS, _ANGLES, _SCALES, _DIFFS
tile = _TILES[ii]
image = _IMAGE
opts = _OPTS
pos = _POSS[ii]
try:
# TODO: Add unittests that zero success result
# doesn't influence anything
with reporting.report_wrapper(reports, ii) as wrapped:
resdict = process_images((tile, image), opts,
reports=wrapped)
resdict['tvec'] += pos
if np.isnan(_DIFFS[0]):
_DIFFS[0] = resdict["Dangle"]
_DIFFS[1] = resdict["Dscale"]
_DIFFS[2] = resdict["Dt"]
except ValueError:
# probably incompatible images due to high scale change, so we
# just add some harmless stuff here and proceed.
resdict = dict(success=0)
_distribute_resdict(resdict, ii)
_SUCCS[ii] = resdict["success"]
if _SUCCS[ii] > 0:
# print("%d: succ: %g" % (ii, resdict["success"]))
# import pylab as pyl
resdict["tvec"] -= pos
tosa = ird.transform_img_dict(image, resdict, 0, opts["order"])
tosa = utils.unextend_by(tosa, opts["extend"])
def _postprocess_unextend(ims, im2, extend, rcoef=1):
if rcoef != 1:
ims = [resample(img, 1.0 / rcoef) for img in ims]
im2 = resample(im2, 1.0 / rcoef)
ret = [utils.unextend_by(img, extend) for img in ims + [im2]]
return ret
def _postprocess_unextend(ims, im2, extend, rcoef=1):
if rcoef != 1:
ims = [resample(img, 1.0 / rcoef) for img in ims]
im2 = resample(im2, 1.0 / rcoef)
ret = [utils.unextend_by(img, extend)
for img in ims + [im2]]
return ret
def process_images(ims, opts, tosa=None):
# lazy import so no imports before run() is really called
import numpy as np
from imreg_dft import utils
from imreg_dft import imreg
ims = [utils.extend_by(img, opts["extend"]) for img in ims]
bigshape = np.array([img.shape for img in ims]).max(0)
ims = filter_images(ims, opts["low"], opts["high"])
rcoef = opts["resample"]
if rcoef != 1:
ims = [resample(img, rcoef) for img in ims]
bigshape *= rcoef
# Make the shape of images the same
ims = [
utils.embed_to(np.zeros(bigshape) + utils.get_borderval(img, 5), img)
for img in ims
]
resdict = imreg.similarity(ims[0], ims[1], opts["iters"], opts["order"],
opts["constraints"], opts["filter_pcorr"],
opts["exponent"])
im2 = resdict.pop("timg")
# Seems that the reampling simply scales the translation
resdict["tvec"] /= rcoef
ty, tx = resdict["tvec"]
resdict["tx"] = tx
resdict["ty"] = ty
resdict["imgs"] = ims
tform = resdict
if tosa is not None:
tosa[:] = ird.transform_img_dict(tosa, tform)
if rcoef != 1:
ims = [resample(img, 1.0 / rcoef) for img in ims]
im2 = resample(im2, 1.0 / rcoef)
resdict["Dt"] /= rcoef
resdict["unextended"] = [
utils.unextend_by(img, opts["extend"]) for img in ims + [im2]
]
return resdict
def testExtend(self):
what = np.random.random((20, 11))
whaty = what.shape[0]
what[:] += np.arange(whaty, dtype=float)[:, np.newaxis] * 5 / whaty
dftscore0 = self._dftscore(what)
dsts = (2, 3, 4)
for dst in dsts:
ext = utils.extend_by(what, dst)
# Bigger distance should mean better "DFT score"
dftscore = self._dftscore(ext)
self.assertLess(dftscore, dftscore0 * 1.1)
dftscore0 = dftscore
undone = utils.unextend_by(ext, dst)
self.assertEqual(what.shape, undone.shape)
def testExtend(self):
what = np.random.random((20, 11))
whaty = what.shape[0]
what[:] += np.arange(whaty, dtype=float)[:, np.newaxis] * 5 / whaty
dftscore0 = self._dftscore(what)
dsts = (2, 3, 4)
for dst in dsts:
ext = utils.extend_by(what, dst)
# Bigger distance should mean better "DFT score"
dftscore = self._dftscore(ext)
self.assertLess(dftscore, dftscore0 * 1.1)
dftscore0 = dftscore
undone = utils.unextend_by(ext, dst)
self.assertEqual(what.shape, undone.shape)
def process_images(ims, opts, tosa=None):
# lazy import so no imports before run() is really called
import numpy as np
from imreg_dft import utils
from imreg_dft import imreg
ims = [utils.extend_by(img, opts["extend"]) for img in ims]
bigshape = np.array([img.shape for img in ims]).max(0)
ims = filter_images(ims, opts["low"], opts["high"])
rcoef = opts["resample"]
if rcoef != 1:
ims = [resample(img, rcoef) for img in ims]
bigshape *= rcoef
# Make the shape of images the same
ims = [utils.embed_to(np.zeros(bigshape) + utils.get_borderval(img, 5), img)
for img in ims]
resdict = imreg.similarity(
ims[0], ims[1], opts["iters"], opts["order"], opts["constraints"],
opts["filter_pcorr"], opts["exponent"])
im2 = resdict.pop("timg")
# Seems that the reampling simply scales the translation
resdict["tvec"] /= rcoef
ty, tx = resdict["tvec"]
resdict["tx"] = tx
resdict["ty"] = ty
resdict["imgs"] = ims
tform = resdict
if tosa is not None:
tosa[:] = ird.transform_img_dict(tosa, tform)
if rcoef != 1:
ims = [resample(img, 1.0 / rcoef) for img in ims]
im2 = resample(im2, 1.0 / rcoef)
resdict["Dt"] /= rcoef
resdict["unextended"] = [utils.unextend_by(img, opts["extend"])
for img in ims + [im2]]
return resdict
def _postprocess_unextend(ims, im2, extend):
ret = [utils.unextend_by(img, extend) for img in ims + [im2]]
return ret
